Reviews: Minimax Optimal Estimation of Approximate Differential Privacy on Neighboring Databases

The authors study the rates of estimating approximate differential privacy (aDP). They do so by reformulating it as a property estimation problem. I find this reduction fairly novel and ties DP to a large body of work on polynomial estimation. In property estimation, it is known that carefully trading off the bias and variance via polynomial approximation, particularly in regions of low probability, allows for obtaining the optimal min max rates. The authors follow the same recipe and show that the min max error scales as Se \epsilon / n \log n.